Brain Inspired Computing
Dozent: Baumbach
Link zum LSF
32 Teilnehmer/innen
In the course "Brain-inspired computing", we give an introduction to biophysical models of nerve cells (neurons) and explore principles of computation and self-organization (learning) in biological and artificial neural networks.
Starting from the ionic current flow across cell membranes, we retrace the Nobel-price winning work of Alan Hodgkin and Andrew Huxley to extract simplified, mathematically tractable models of neural input integration and the generation of neural responses, so-called action potentials. We study signaling between neurons via chemical synapses and analyze the firing response of leaky integrate and firing (LIF) models to spatio-temporal input. Since such a low level description quickly becomes intractable in larger networks, we characterize network evolution and neural coding by statistical methods, such as Fokker-Plank equations, auto- and cross-correlation functions and dynamical systems theory. We then turn to the wide field of neural plasticity, that is, the brain's fascinating ability of self-organized adaptation and learning. We review experimental findings, functional plasticity models and emergent computational capabilities of neural networks with a particular focus on short-term and long-term synaptic plasticity. We touch on the design of artificial physical implementations of simplified neuron- and synapse models in micro-electronic circuitry, enabling the development of novel high-performance neuro-inspired computing platforms. Finally, we examine deep conceptual similarities between information processing and learning in spiking neural networks on the one hand, and principles of Bayesian computation and machine learning on the other, by the examples of multilayer perceptron networks and Boltzmann machines.
A recurring mathematical challenge in computational neuroscience, that will also accompany us throughout the course, is the development of a consistent mathematical description across multiple levels of abstraction, in order to find a balance between biophysical accuracy and analytical tractability of a complex system. Students are expected to be familiar with calculus and linear algebra. For computer simulations, which take a significant share of the exercises, at least basic knowledge in the Python programming language is strongly recommended.
Addendum: The interest in this course seems to be quite high. If the exercise groups end up being completely filled again, please feel free to just show up to the first lecture. Depending on the actual level of interest we will add an additional exercise group.
Materialien
Zoom Details
- Einwahl: https://us02web.zoom.us/j/86561968018?pwd=RCtGNklrRXhwaUxuWVNBTmtBaWhNUT09
- Meeting ID: 865 6196 8018
- Passcode: 813812
BrainScaleS-2 demos
- https://github.com/electronicvisions/brainscales2-demos/tree/jupyter-notebooks
Exam
- Date: 18.2.22; 14:00-16:00 Place: INF 227 (KIP) HS1
Lecture Notes
Lecture 14
- GMT20220201-131506_Recording_1280x720.mp4
- blackboard_L14.pdf
- Link to the video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/a9bf34eb-abea-4a9d-8330-35d1ad11035b
Lecture 13
- GMT20220125-131553_Recording_1920x1080.mp4
- slides_LIF_sampling.pdf
- blackboard_L13.pdf
- planned_script_L13.pdf
- Link to the video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/4fe6f21f-c144-4be5-babb-3350279a52f3
Lecture 12
- GMT20220118-131712_Recording_1920x1080_faststart.mp4
- slides_phenomenological.pdf
- sta_demo.ipynb
- blackboard_phenomenological.pdf
- planned_script_phenomenological.pdf
- Link to the video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/0692b44d-8fbd-48d3-aa5f-18a07541508c
Lecture 11
- GMT20220111-131459_Recording_1280x664.mp4
- blackboard_plasticity.pdf
- planned_script_plasticity.pdf
- Link to the video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/b6d652b7-b899-4ccb-9ccd-535ef232d388
Lecture 10
- GMT20211221-132413_Recording_1920x1080.mp4
- slides_NMHW.pdf
- Blackboard_NMHW.pdf
- planned_script_nmhw.pdf
- Link to the video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/9d8313cc-fa05-4dec-91a2-c3c1e690d174
Lecture 09
- GMT20211214-131650_Recording_1280x720.mp4
- blackboard_Lecture_09.pdf
- planned_script_L09.pdf
- Link to the video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/9bde201d-8bee-40f1-8e6d-77a8a5b7f5d1
Lecture 08
- blackboard_Lecture_08.pdf
- planned_script_08.pdf
- GMT20211207-131732_Recording_1920x1080.mp4
- Link to video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/dc6222f4-3f65-4ba4-b6ce-dec58e37ce4e
Lecture 07
- blackboard_Lecture_07.pdf
- planned_script_L07.pdf
- GMT20211130-131127_Recording_1920x1080.mp4
- Link to video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/3b4ba0eb-7ef0-4b12-b03e-3bee701e9709
Lecture 06
- blackboard_L06.pdf
- planned_script_lif_neurons_L06.pdf
- Link to video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/18ee690c-61a7-4898-ad87-a5d81a563a55
Lecture 05
- BIC2021_-_Lecture_05.pdf
- planned_script_L05.pdf
- 20211116_BIC_Recording_1280x720.mp4
- Link to video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/0ad3a920-2a86-4543-ba45-ff7425eee1a1
Lecture 04
- BIC2021_-_Lecture_04.pdf
- planned_script_L04.pdf
- BIC_20211109_1280x664.mp4
- Link to video of lecture 04: https://data-proxy.ebrains.eu/api/permalinks/2d1f587f-f121-4178-ba0c-d40189a9e3b1
Lecture 03
- script_03_planned_script.pdf
- script_03_blackboard.pdf
- slides_03.pdf
- script_03_neuronmodels_prelecture.pdf
Lecture 02
- script_02_electrophysiology.pdf
- Link to video of the lecture: https://data-proxy.ebrains.eu/api/permalinks/953488f8-fc26-4db5-8906-61be9fcb013a
- 20211026_BIC_Recording_640x360_faststart.mp4
- slides_01_Introduction.pdf
Broken Things please ignore
- BIC - Lecture 07.pdf
Übungsblätter
- 01
- 02
- 03
- 04
- 05
- 06
- 07
- 08
- NMHW
- 09
- 10
- 11
Übungsgruppen
- Gruppe G2 (Timo Gierlich)
12 Teilnehmer/innen
INF 227 / SR 3.403, Fr 11:00 - 13:00 - Gruppe G1 (Julian Göltz)
11 Teilnehmer/innen
INF 227 / SR 3.404, Mo 14:00 - 16:00 - Gruppe G3 (Philipp Spilger)
9 Teilnehmer/innen
INF 227 / SR 3.404, Mo 16:00 - 18:00